Sentence examples for between a target variable from inspiring English sources

In this study, a mixed geographically weighted regression (MGWR) method which can deal with fixed and varying spatial relationships between a target variable and its environmental variables were proposed and used to predict topsoil soil organic matter (SOM) concentration in two study areas (Heshan, Heilongjiang province and Xuancheng, Anhui province, China) at two scales.

Posterior values are between 0 and 1, where 0 indicates no relevance, 1 indicates 100% relevance between a predictor and a target variable.

Interval plots are measured at least twice with the objective of assessing the rate of change of a target variable between successive re-measurements.

Example A: The target variable is x.

aWith partial diagrams the association between the target variable and a specific predicting variable is investigated without including the effect of the other predicting variables on the target variable.

A model for the relationship between the target variable and one or more auxiliary variable(s) can adequately conform to the trend in Y. Auxiliary data are commonly available for all population elements.

Under the assumption that the relationship between the target variable being downscaled and the available covariates can be nonlinear, dissever uses weighted generalised additive models (GAMs) to drive the empirical function.

All gave more efficient results than the use of simple random sampling as long as there was some positive level of correlation between the target variable of the inventory and the auxiliary variable being used; their efficiency increased progressively as the level of correlation increased.

R Statistics 2.14 (R Development Core Team, Vienna, Austria) was used to calculate the Pearson correlation between the target variable (NTD incidences) and the area proportion of each type of soils and lithodological classes across a series of buffer distances (from 20 m to 3 km).

The high nonlinearity of the features (meaning low correlation between target variable and features) restricts the number of algorithms we can use to predict with high accuracy.

The relationship between target variable and input variable X1 and X2 is nonlinear.